In this paper, a novel particle swarm optimization (PSO) algorithm is proposed in order to improve the accuracy of the traditional support vector machine (SVM) approaches with applications in analyzing data of oil pipeline leak detection. In the proposed saturated and mix-delayed particle swarm optimization (SMDPSO) algorithm, the evolutionary state is determined by evaluating the evolutionary factor in each iteration, based on which the velocity updating model switches from one to another. With the purpose of reducing the possibility of getting trapped in the local optima and also expanding the search space, time-varying time-delays and distributed time-delays are introduced in the velocity updating model to respectively reflect the history of previous personal and global optimum particles. The introduction of saturation constraint ensures that the particles will convergence in case that the velocity of the particles is too large. Eight well-known benchmark functions are employed to evaluate the proposed SMDPSO algorithm which is shown via extensive comparisons to outperform some currently popular PSO algorithms. To further illustrate the application potential, the developed framework SMDPSO-based SVM algorithm is exploited in the problem of oil pipeline leak detection. Experiment results demonstrate that the SMDPSO-based SVM method is superior over other well-known classification algorithms.
This paper addresses the H ∞ -consensus control problem for a class of discrete-time nonlinear multiagent systems with missing measurements. First of all, the multi-agent systems under consideration are composed of N followers and a leader, where all agents are of nonlinear characteristics and all followers need to track the leader. Next, the measurements of all agents are not reliable, which could cause the occurrence of the missing measurements. In order to mitigate the computation burden stemming from the complicated couplings among the agents, the spectral decomposition and coordinate transformation are introduced to decouple the closed-loop systems into multiple independent subsystems. Then, a sufficient condition is established for the existence of the H ∞consensus controller ensuring that the closed-loop multi-agent system achieves the H ∞ -consensus performance by means of the Lyapunov stability theory. Besides, the controller parameters are obtained by solving some linear matrix inequalities. Finally, a simulation example is utilized to illustrate the effectiveness of the proposed control algorithm.
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